Process Parameters Optimization for Multiple Quality Characteristics in Plastic Injection Molding using Taguchi Method, BPNN, GA, and Hybrid PSO-GA

This paper presents a two stage optimization system to find optimal process parameters of multiple quality characteristics in plastic injection molding. Taguchi method, Back-Propagation Neural Network (BPNN), Genetic Algorithm (GA), and combination of Particle Swarm Optimization and Genetic Algorithm (PSO-GA) are used in this study to find optimum parameter settings. Melt temperature, injection velocity, packing pressure, packing time, and cooling time are selected as initial process parameters in the experiment. First, experimental work is conducted using Taguchi orthogonal array. According to the result from the Taguchi experiment, S/N ratio is calculated to find the best combination settings for product quality. Then, ANOVA is used to determine significant factors of the control parameters. Moreover the S/N ratio predictor and quality predictor are constructed using BPNN: In the first stage optimization, S/N ratio predictor and GA are used to reduce variance of quality characteristic. In the second stage optimization, the S/N ratio predictor and quality predictor with hybrid PSO-GA are used to find optimal parameter settings for quality characteristic and stability of the process. Finally, three confirmation experiments are conducted to assess the effectiveness of the proposed system. Upon optimization, it is seen that the proposed system not only improves the quality of plastic parts, but also reduces variability of the process effectively.